Overview

Dataset statistics

Number of variables37
Number of observations697802
Missing cells809
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory197.0 MiB
Average record size in memory296.0 B

Variable types

CAT29
NUM6
UNSUPPORTED2

Warnings

Tags has constant value "697802" Constant
Source Database has constant value "697802" Constant
Systematic Name Interactor A has a high cardinality: 6052 distinct values High cardinality
Systematic Name Interactor B has a high cardinality: 7884 distinct values High cardinality
Official Symbol Interactor A has a high cardinality: 17561 distinct values High cardinality
Official Symbol Interactor B has a high cardinality: 23156 distinct values High cardinality
Synonyms Interactor A has a high cardinality: 15264 distinct values High cardinality
Synonyms Interactor B has a high cardinality: 19520 distinct values High cardinality
Author has a high cardinality: 30284 distinct values High cardinality
Publication Source has a high cardinality: 32416 distinct values High cardinality
Qualifications has a high cardinality: 11876 distinct values High cardinality
SWISS-PROT Accessions Interactor A has a high cardinality: 17157 distinct values High cardinality
TREMBL Accessions Interactor A has a high cardinality: 7118 distinct values High cardinality
REFSEQ Accessions Interactor A has a high cardinality: 17535 distinct values High cardinality
SWISS-PROT Accessions Interactor B has a high cardinality: 21620 distinct values High cardinality
TREMBL Accessions Interactor B has a high cardinality: 8680 distinct values High cardinality
REFSEQ Accessions Interactor B has a high cardinality: 22371 distinct values High cardinality
Ontology Term IDs has a high cardinality: 480 distinct values High cardinality
Ontology Term Names has a high cardinality: 474 distinct values High cardinality
Ontology Term Categories has a high cardinality: 104 distinct values High cardinality
Ontology Term Qualifier IDs has a high cardinality: 119 distinct values High cardinality
Ontology Term Qualifier Names has a high cardinality: 118 distinct values High cardinality
Organism Name Interactor A has a high cardinality: 55 distinct values High cardinality
Organism Name Interactor B has a high cardinality: 52 distinct values High cardinality
Organism ID Interactor A is highly correlated with BioGRID ID Interactor AHigh correlation
BioGRID ID Interactor A is highly correlated with Organism ID Interactor AHigh correlation
Experimental System Type is highly correlated with Experimental SystemHigh correlation
Experimental System is highly correlated with Experimental System TypeHigh correlation
BioGRID ID Interactor B is highly skewed (γ1 = 20.03093449) Skewed
Organism ID Interactor B is highly skewed (γ1 = 45.9750313) Skewed
#BioGRID Interaction ID has unique values Unique
Entrez Gene Interactor A is an unsupported type, check if it needs cleaning or further analysis Unsupported
Score is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2020-11-18 22:48:32.888194
Analysis finished2020-11-18 22:49:44.624819
Duration1 minute and 11.74 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

#BioGRID Interaction ID
Real number (ℝ≥0)

UNIQUE

Distinct697802
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1791418.342
Minimum103
Maximum2876329
Zeros0
Zeros (%)0.0%
Memory size5.3 MiB
2020-11-18T23:50:58.845186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum103
5-th percentile314839.05
Q1923460.25
median2226809.5
Q32635824.75
95-th percentile2824468.95
Maximum2876329
Range2876226
Interquartile range (IQR)1712364.5

Descriptive statistics

Standard deviation889127.0724
Coefficient of variation (CV)0.4963257614
Kurtosis-1.505500948
Mean1791418.342
Median Absolute Deviation (MAD)620866
Skewness-0.2900035311
Sum1.250055302e+12
Variance7.905469509e+11
MonotocityStrictly increasing
2020-11-18T23:50:58.979389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
10506231< 0.1%
 
9044671< 0.1%
 
8942381< 0.1%
 
27924221< 0.1%
 
8921851< 0.1%
 
22393271< 0.1%
 
9126631< 0.1%
 
22269991< 0.1%
 
27244831< 0.1%
 
9147081< 0.1%
 
Other values (697792)697792> 99.9%
 
ValueCountFrequency (%) 
1031< 0.1%
 
1171< 0.1%
 
1831< 0.1%
 
2781< 0.1%
 
4181< 0.1%
 
ValueCountFrequency (%) 
28763291< 0.1%
 
28763281< 0.1%
 
28763271< 0.1%
 
28763261< 0.1%
 
28763251< 0.1%
 

Entrez Gene Interactor A
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size5.3 MiB

Entrez Gene Interactor B
Real number (ℝ≥0)

Distinct23834
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean413409.8477
Minimum1
Maximum107983993
Zeros0
Zeros (%)0.0%
Memory size5.3 MiB
2020-11-18T23:50:59.134018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile973
Q15394
median9631
Q355095
95-th percentile220929
Maximum107983993
Range107983992
Interquartile range (IQR)49701

Descriptive statistics

Standard deviation6027665.675
Coefficient of variation (CV)14.58036307
Kurtosis271.5020809
Mean413409.8477
Median Absolute Deviation (MAD)7540
Skewness16.51891519
Sum2.884782185e+11
Variance3.633275349e+13
MonotocityNot monotonic
2020-11-18T23:50:59.489115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
35120980.3%
 
715718660.3%
 
731613910.2%
 
41939820.1%
 
30659460.1%
 
33208850.1%
 
33128700.1%
 
20337560.1%
 
74157250.1%
 
14997170.1%
 
Other values (23824)68656698.4%
 
ValueCountFrequency (%) 
112< 0.1%
 
263< 0.1%
 
94< 0.1%
 
1019< 0.1%
 
1230< 0.1%
 
ValueCountFrequency (%) 
1079839933< 0.1%
 
1072820921< 0.1%
 
1043552951< 0.1%
 
1043551331< 0.1%
 
1043260581< 0.1%
 

BioGRID ID Interactor A
Real number (ℝ≥0)

HIGH CORRELATION

Distinct17907
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean257469.8067
Minimum1582
Maximum4383954
Zeros0
Zeros (%)0.0%
Memory size5.3 MiB
2020-11-18T23:50:59.643656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1582
5-th percentile107331
Q1110846
median114798
Q3120903
95-th percentile214351
Maximum4383954
Range4382372
Interquartile range (IQR)10057

Descriptive statistics

Standard deviation740311.4648
Coefficient of variation (CV)2.875333129
Kurtosis26.50037553
Mean257469.8067
Median Absolute Deviation (MAD)4797
Skewness5.309161984
Sum1.79662946e+11
Variance5.48061065e+11
MonotocityNot monotonic
2020-11-18T23:50:59.774969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
11745230490.4%
 
12169729710.4%
 
10840326240.4%
 
11069425210.4%
 
11350024980.4%
 
10840423290.3%
 
438387122230.3%
 
10827621030.3%
 
11301020700.3%
 
438384620590.3%
 
Other values (17897)67335596.5%
 
ValueCountFrequency (%) 
15821< 0.1%
 
23971< 0.1%
 
28561< 0.1%
 
29181< 0.1%
 
32041< 0.1%
 
ValueCountFrequency (%) 
43839546190.1%
 
438395310< 0.1%
 
43839523< 0.1%
 
43839519< 0.1%
 
438395083< 0.1%
 

BioGRID ID Interactor B
Real number (ℝ≥0)

SKEWED

Distinct23834
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127133.8181
Minimum104
Maximum4383941
Zeros0
Zeros (%)0.0%
Memory size5.3 MiB
2020-11-18T23:50:59.923339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum104
5-th percentile107343
Q1111362
median114954
Q3120446
95-th percentile129022
Maximum4383941
Range4383837
Interquartile range (IQR)9084

Descriptive statistics

Standard deviation147327.3734
Coefficient of variation (CV)1.158837009
Kurtosis466.2855914
Mean127133.8181
Median Absolute Deviation (MAD)4420
Skewness20.03093449
Sum8.871423251e+10
Variance2.170535496e+10
MonotocityNot monotonic
2020-11-18T23:51:00.061214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
10684820980.3%
 
11301018660.3%
 
11316413910.2%
 
1103589820.1%
 
1093159460.1%
 
1095528850.1%
 
1095448700.1%
 
1083477560.1%
 
1132587250.1%
 
1078807170.1%
 
Other values (23824)68656698.4%
 
ValueCountFrequency (%) 
1041< 0.1%
 
3951< 0.1%
 
16711< 0.1%
 
19331< 0.1%
 
25521< 0.1%
 
ValueCountFrequency (%) 
43839412< 0.1%
 
43839231< 0.1%
 
43839221< 0.1%
 
43839206< 0.1%
 
43839193< 0.1%
 

Systematic Name Interactor A
Categorical

HIGH CARDINALITY

Distinct6052
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
-
478496 
MSTP054
 
3049
RP1-130E4.1
 
2624
GU280_gp08
 
2223
GU280_gp05
 
2059
Other values (6047)
209351 
ValueCountFrequency (%) 
-47849668.6%
 
MSTP05430490.4%
 
RP1-130E4.126240.4%
 
GU280_gp0822230.3%
 
GU280_gp0520590.3%
 
GU280_gp01_nsp418410.3%
 
GU280_gp01_nsp615850.2%
 
P/OKcl.1415410.2%
 
GU280_gp0715160.2%
 
RES4-2614120.2%
 
Other values (6042)20145628.9%
 
2020-11-18T23:51:00.229395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1180 ?
Unique (%)0.2%
2020-11-18T23:51:00.372730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length25
Median length1
Mean length4.129187076
Min length1

Systematic Name Interactor B
Categorical

HIGH CARDINALITY

Distinct7884
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
-
493420 
RP4-811H24.2
 
946
RP1-85F18.1
 
756
OK/SW-cl.35
 
717
RP11-174I10.1
 
533
Other values (7879)
201430 
ValueCountFrequency (%) 
-49342070.7%
 
RP4-811H24.29460.1%
 
RP1-85F18.17560.1%
 
OK/SW-cl.357170.1%
 
RP11-174I10.15330.1%
 
LA16c-313D11.65120.1%
 
CTA-216E10.75100.1%
 
RP1-302G2.14960.1%
 
RP11-125A15.24950.1%
 
RP11-575L7.14930.1%
 
Other values (7874)19892428.5%
 
2020-11-18T23:51:00.531901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2549 ?
Unique (%)0.4%
2020-11-18T23:51:00.677835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length25
Median length1
Mean length3.852918163
Min length1

Official Symbol Interactor A
Categorical

HIGH CARDINALITY

Distinct17561
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
KIAA1429
 
3049
PLEKHA4
 
2971
ESR1
 
2624
MYC
 
2521
TRIM25
 
2498
Other values (17556)
684139 
ValueCountFrequency (%) 
KIAA142930490.4%
 
PLEKHA429710.4%
 
ESR126240.4%
 
MYC25210.4%
 
TRIM2524980.4%
 
ORF7b24760.4%
 
ESR223290.3%
 
M21850.3%
 
EGFR21030.3%
 
TP5320710.3%
 
Other values (17551)67297596.4%
 
2020-11-18T23:51:00.860246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2527 ?
Unique (%)0.4%
2020-11-18T23:51:01.003238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length5
Mean length5.084108386
Min length1

Official Symbol Interactor B
Categorical

HIGH CARDINALITY

Distinct23156
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
APP
 
2098
TP53
 
1868
UBC
 
1399
MDM2
 
982
HDAC1
 
946
Other values (23151)
690509 
ValueCountFrequency (%) 
APP20980.3%
 
TP5318680.3%
 
UBC13990.2%
 
MDM29820.1%
 
HDAC19460.1%
 
HSP90AA18880.1%
 
HSPA88750.1%
 
EP3007560.1%
 
VCP7250.1%
 
CTNNB17170.1%
 
Other values (23146)68654898.4%
 
2020-11-18T23:51:01.182302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5630 ?
Unique (%)0.8%
2020-11-18T23:51:01.317457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length5
Mean length5.223609276
Min length1

Synonyms Interactor A
Categorical

HIGH CARDINALITY

Distinct15264
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
-
 
54173
fSAP121
 
3049
PEPP1
 
2971
ER|ESR|ESRA|ESTRR|Era|NR3A1
 
2624
MRTL|MYCC|bHLHe39|c-Myc
 
2521
Other values (15259)
632464 
ValueCountFrequency (%) 
-541737.8%
 
fSAP12130490.4%
 
PEPP129710.4%
 
ER|ESR|ESRA|ESTRR|Era|NR3A126240.4%
 
MRTL|MYCC|bHLHe39|c-Myc25210.4%
 
EFP|RNF147|Z147|ZNF14724980.4%
 
ER-BETA|ESR-BETA|ESRB|ESTRB|Erb|NR3A223290.3%
 
SARS-CoV2 ORF7b|SARS-CoV-2 ORF7b|7b|NS7B_SARS2|PRO_000044979922230.3%
 
ERBB|ERBB1|HER1|NISBD2|PIG61|mENA21030.3%
 
BCC7|LFS1|P53|TRP5320700.3%
 
Other values (15254)62124189.0%
 
2020-11-18T23:51:01.478283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2113 ?
Unique (%)0.3%
2020-11-18T23:51:01.761594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length258
Median length17
Mean length22.17586651
Min length1

Synonyms Interactor B
Categorical

HIGH CARDINALITY

Distinct19520
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
-
 
61246
AAA|ABETA|ABPP|AD1|APPI|CTFgamma|CVAP|PN-II|PN2
 
2098
BCC7|LFS1|P53|TRP53
 
1866
HMG20
 
1391
ACTFS|HDMX|hdm2
 
982
Other values (19515)
630219 
ValueCountFrequency (%) 
-612468.8%
 
AAA|ABETA|ABPP|AD1|APPI|CTFgamma|CVAP|PN-II|PN220980.3%
 
BCC7|LFS1|P53|TRP5318660.3%
 
HMG2013910.2%
 
ACTFS|HDMX|hdm29820.1%
 
GON-10|HD1|RPD3|RPD3L19460.1%
 
EL52|HSP86|HSP89A|HSP90A|HSP90N|HSPC1|HSPCA|HSPCAL1|HSPCAL4|HSPN|Hsp89|Hsp90|LAP-2|LAP28850.1%
 
HEL-33|HEL-S-72p|HSC54|HSC70|HSC71|HSP71|HSP73|HSPA10|LAP-1|LAP1|NIP718700.1%
 
KAT3B|RSTS2|p3007560.1%
 
ALS14|HEL-220|HEL-S-70|IBMPFD|IBMPFD1|TERA|p977250.1%
 
Other values (19510)62603789.7%
 
2020-11-18T23:51:01.957255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4618 ?
Unique (%)0.7%
2020-11-18T23:51:02.124441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length463
Median length16
Mean length19.49409861
Min length1

Experimental System
Categorical

HIGH CORRELATION

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
Affinity Capture-MS
302710 
Two-hybrid
112942 
Affinity Capture-Western
70201 
Proximity Label-MS
68665 
Co-fractionation
45674 
Other values (22)
97610 
ValueCountFrequency (%) 
Affinity Capture-MS30271043.4%
 
Two-hybrid11294216.2%
 
Affinity Capture-Western7020110.1%
 
Proximity Label-MS686659.8%
 
Co-fractionation456746.5%
 
Reconstituted Complex369365.3%
 
Affinity Capture-RNA132711.9%
 
Biochemical Activity126921.8%
 
Protein-RNA72821.0%
 
Negative Genetic45540.7%
 
Other values (17)228753.3%
 
2020-11-18T23:51:02.265037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-18T23:51:02.397337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length29
Median length19
Mean length17.68705593
Min length3

Experimental System Type
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
physical
686776 
genetic
 
11026
ValueCountFrequency (%) 
physical68677698.4%
 
genetic110261.6%
 
2020-11-18T23:51:02.514708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-18T23:51:02.591786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:51:02.677215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length7.984198956
Min length7

Author
Categorical

HIGH CARDINALITY

Distinct30284
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
Huttlin EL (2017)
56473 
Luck K (2020)
52414 
Hein MY (2015)
 
29164
Huttlin EL (2015)
 
23712
Wan C (2015)
 
16795
Other values (30279)
519244 
ValueCountFrequency (%) 
Huttlin EL (2017)564738.1%
 
Luck K (2020)524147.5%
 
Hein MY (2015)291644.2%
 
Huttlin EL (2015)237123.4%
 
Wan C (2015)167952.4%
 
Antonicka H (2020)147722.1%
 
Rolland T (2014)139402.0%
 
Havugimana PC (2012)139182.0%
 
Capalbo L (2019)89891.3%
 
Samavarchi-Tehrani P (2020)78101.1%
 
Other values (30274)45981565.9%
 
2020-11-18T23:51:02.858709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique6944 ?
Unique (%)1.0%
2020-11-18T23:51:03.000901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length37
Median length15
Mean length15.59832015
Min length10

Publication Source
Categorical

HIGH CARDINALITY

Distinct32416
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
PUBMED:28514442
56473 
PUBMED:32296183
52414 
PUBMED:26496610
 
29164
PUBMED:26186194
 
23712
PUBMED:26344197
 
16795
Other values (32411)
519244 
ValueCountFrequency (%) 
PUBMED:28514442564738.1%
 
PUBMED:32296183524147.5%
 
PUBMED:26496610291644.2%
 
PUBMED:26186194237123.4%
 
PUBMED:26344197167952.4%
 
PUBMED:32877691147722.1%
 
PUBMED:25416956139402.0%
 
PUBMED:22939629139182.0%
 
PUBMED:3158607389891.3%
 
DOI:10.1101/2020.09.03.28210378101.1%
 
Other values (32406)45981565.9%
 
2020-11-18T23:51:03.199442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique7616 ?
Unique (%)1.1%
2020-11-18T23:51:03.339114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length31
Median length15
Mean length15.37272321
Min length11

Organism ID Interactor A
Real number (ℝ≥0)

HIGH CORRELATION

Distinct55
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87989.69908
Minimum3702
Maximum2697049
Zeros0
Zeros (%)0.0%
Memory size5.3 MiB
2020-11-18T23:51:03.466631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3702
5-th percentile9606
Q19606
median9606
Q39606
95-th percentile10090
Maximum2697049
Range2693347
Interquartile range (IQR)0

Descriptive statistics

Standard deviation443491.2533
Coefficient of variation (CV)5.040263325
Kurtosis30.23109541
Mean87989.69908
Median Absolute Deviation (MAD)0
Skewness5.655180996
Sum6.1399388e+10
Variance1.966844918e+11
MonotocityNot monotonic
2020-11-18T23:51:03.611906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
960664968093.1%
 
2697049193362.8%
 
10090189822.7%
 
55929223780.3%
 
1167616700.2%
 
69400912470.2%
 
101168760.1%
 
372967840.1%
 
111035170.1%
 
103763600.1%
 
Other values (45)19720.3%
 
ValueCountFrequency (%) 
370265< 0.1%
 
39883< 0.1%
 
41131< 0.1%
 
623936< 0.1%
 
7227214< 0.1%
 
ValueCountFrequency (%) 
2697049193362.8%
 
1335626306< 0.1%
 
69400912470.2%
 
55929223780.3%
 
5111452< 0.1%
 

Organism ID Interactor B
Real number (ℝ≥0)

SKEWED

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11102.7769
Minimum3702
Maximum2697049
Zeros0
Zeros (%)0.0%
Memory size5.3 MiB
2020-11-18T23:51:03.753235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3702
5-th percentile9606
Q19606
median9606
Q39606
95-th percentile9606
Maximum2697049
Range2693347
Interquartile range (IQR)0

Descriptive statistics

Standard deviation35650.867
Coefficient of variation (CV)3.210986524
Kurtosis3028.190117
Mean11102.7769
Median Absolute Deviation (MAD)0
Skewness45.9750313
Sum7747539926
Variance1270984318
MonotocityNot monotonic
2020-11-18T23:51:03.899726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
960668901098.7%
 
1009043210.6%
 
55929211150.2%
 
101169910.1%
 
2848125860.1%
 
116763630.1%
 
9913147< 0.1%
 
3702129< 0.1%
 
10298110< 0.1%
 
722786< 0.1%
 
Other values (42)9440.1%
 
ValueCountFrequency (%) 
3702129< 0.1%
 
39882< 0.1%
 
45771< 0.1%
 
623917< 0.1%
 
722786< 0.1%
 
ValueCountFrequency (%) 
269704963< 0.1%
 
13356267< 0.1%
 
69400949< 0.1%
 
55929211150.2%
 
5111451< 0.1%
 

Throughput
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
High Throughput
533242 
Low Throughput
163319 
High Throughput|Low Throughput
 
1241
ValueCountFrequency (%) 
High Throughput53324276.4%
 
Low Throughput16331923.4%
 
High Throughput|Low Throughput12410.2%
 
2020-11-18T23:51:04.167869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-18T23:51:04.252231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:51:04.360884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length15
Mean length14.79262885
Min length14

Score
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size5.3 MiB

Modification
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
-
685110 
Phosphorylation
 
5668
Ubiquitination
 
3726
Deubiquitination
 
613
Acetylation
 
570
Other values (14)
 
2115
ValueCountFrequency (%) 
-68511098.2%
 
Phosphorylation56680.8%
 
Ubiquitination37260.5%
 
Deubiquitination6130.1%
 
Acetylation5700.1%
 
Proteolytic Processing4510.1%
 
Sumoylation3850.1%
 
Methylation332< 0.1%
 
Dephosphorylation268< 0.1%
 
No Modification229< 0.1%
 
Other values (9)4500.1%
 
2020-11-18T23:51:04.498079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-18T23:51:04.627509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length1
Mean length1.247262117
Min length1

Qualifications
Categorical

HIGH CARDINALITY

Distinct11876
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
-
326390 
This human reference interactome (HuRI) was generated by performing nine two-hybrid screens with the high confidence interactions determined by pairwise verification by quadruplicate retesting and sequence confirmation. This HI-III-20 dataset contains over 52,000 PPIs involving more than 8,000 proteins.
52414 
Quantitative scores are a modified CompPASS score derived from Sowa et al., Cell, 2009 (PMID 19615732). The cut-off threshold is > 0.75.
45305 
interaction detected by quantitative BAC-GFP interactomics (QUBIC)
 
29161
Interaction also contained in second paper after re-analysis of the data, Architecture of the human interactome defines protein communities and disease networks, PUBMED:28514442|Quantitative scores are a modified CompPASS score derived from Sowa et al., Cell, 2009 (PMID 19615732). The cut-off threshold is > 0.75.
 
17440
Other values (11871)
227092 
ValueCountFrequency (%) 
-32639046.8%
 
This human reference interactome (HuRI) was generated by performing nine two-hybrid screens with the high confidence interactions determined by pairwise verification by quadruplicate retesting and sequence confirmation. This HI-III-20 dataset contains over 52,000 PPIs involving more than 8,000 proteins.524147.5%
 
Quantitative scores are a modified CompPASS score derived from Sowa et al., Cell, 2009 (PMID 19615732). The cut-off threshold is > 0.75.453056.5%
 
interaction detected by quantitative BAC-GFP interactomics (QUBIC)291614.2%
 
Interaction also contained in second paper after re-analysis of the data, Architecture of the human interactome defines protein communities and disease networks, PUBMED:28514442|Quantitative scores are a modified CompPASS score derived from Sowa et al., Cell, 2009 (PMID 19615732). The cut-off threshold is > 0.75.174402.5%
 
Quantitative scores are a modified CompPASS score derived from Sowa et al., Cell, 2009 (PMID 19615732). The cut-off threshold is > 0.75.|Re-analysis of the data from the first paper, The BioPlex Network: A Systematic Exploration of the Human Interactome, PUBMED:26186194174402.5%
 
Fractionation was combined with mass spectrometry from five diverse animal species to predict co-complex protein interactions conserved across metazoa using an integrative computational scoring procedure along with an SVM approach. The significant data set of 16655 PPI, was derived from a set of more than 1M interactions by examining a ROC curve of predicted interactions against reference annotated complexes at a 67.5% cumulative precision.167952.4%
 
interaction assayed using BioID|interactions were considered high confidence if they had a Bayesian False Discovery Rate of 1% or less|the Saint Score for the interaction (or the maximum of any bait-prey combinations that had multiple scores) is shown145512.1%
 
Denoised score >= 0.75138982.0%
 
BioID83671.2%
 
Other values (11866)15604122.4%
 
2020-11-18T23:51:04.785894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique8606 ?
Unique (%)1.2%
2020-11-18T23:51:04.951691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length731
Median length11
Mean length99.23640374
Min length1

Tags
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
-
697802 
ValueCountFrequency (%) 
-697802100.0%
 
2020-11-18T23:51:05.090131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-18T23:51:05.159595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:51:05.229960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Source Database
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
BIOGRID
697802 
ValueCountFrequency (%) 
BIOGRID697802100.0%
 
2020-11-18T23:51:05.351628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-18T23:51:05.430670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:51:05.511825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length7
Mean length7
Min length7

SWISS-PROT Accessions Interactor A
Categorical

HIGH CARDINALITY

Distinct17157
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
-
 
11926
P0DTD1
 
6551
Q69YN4
 
3049
Q9H4M7
 
2971
P03372
 
2624
Other values (17152)
670681 
ValueCountFrequency (%) 
-119261.7%
 
P0DTD165510.9%
 
Q69YN430490.4%
 
Q9H4M729710.4%
 
P0337226240.4%
 
P0110625210.4%
 
Q1425824980.4%
 
Q9273123290.3%
 
P0DTD822230.3%
 
P0053321030.3%
 
Other values (17147)65900794.4%
 
2020-11-18T23:51:05.684983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2452 ?
Unique (%)0.4%
2020-11-18T23:51:05.828892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length34
Median length6
Mean length5.948281891
Min length1

TREMBL Accessions Interactor A
Categorical

HIGH CARDINALITY

Distinct7118
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
-
364279 
Q9UBT1|G4XH65|A8KAF4
 
2624
Q0PTK2|Q7LCB3|F1D8N3
 
2329
H2EHT1|A0A087WT22|A0A087WXZ1|Q53GA5|A0A087X1Q1|K7PPA8
 
2070
X5DR71
 
1974
Other values (7113)
324526 
ValueCountFrequency (%) 
-36427952.2%
 
Q9UBT1|G4XH65|A8KAF426240.4%
 
Q0PTK2|Q7LCB3|F1D8N323290.3%
 
H2EHT1|A0A087WT22|A0A087WXZ1|Q53GA5|A0A087X1Q1|K7PPA820700.3%
 
X5DR7119740.3%
 
A0A024RAV5|I1SRC5|L7RSL818870.3%
 
B2R4R017370.2%
 
E9PFZ0|A0A024R1V017190.2%
 
Q6NTA215410.2%
 
B7Z600|A0A024R47513920.2%
 
Other values (7108)31625045.3%
 
2020-11-18T23:51:05.989278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique792 ?
Unique (%)0.1%
2020-11-18T23:51:06.185766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length200
Median length1
Mean length6.30176755
Min length1

REFSEQ Accessions Interactor A
Categorical

HIGH CARDINALITY

Distinct17535
Distinct (%)2.5%
Missing211
Missing (%)< 0.1%
Memory size5.3 MiB
-
 
9376
NP_056311|NP_892121
 
3049
NP_001154826|NP_065955
 
2971
NP_001116213|NP_000116|NP_001278159|NP_001278170|NP_001116212|NP_001116214
 
2624
NP_002458|NP_001341799
 
2521
Other values (17530)
677050 
ValueCountFrequency (%) 
-93761.3%
 
NP_056311|NP_89212130490.4%
 
NP_001154826|NP_06595529710.4%
 
NP_001116213|NP_000116|NP_001278159|NP_001278170|NP_001116212|NP_00111621426240.4%
 
NP_002458|NP_00134179925210.4%
 
NP_00507324980.4%
 
NP_001201832|NP_001428|NP_001201831|NP_001278641|NP_001035365|NP_001278652|NP_001258805|NP_00125880623290.3%
 
YP_00972531822230.3%
 
NP_001333829|NP_001333828|NP_958440|NP_005219|NP_001333827|NP_001333826|NP_958441|NP_001333870|NP_95843921030.3%
 
NP_001119586|NP_001119590|NP_001119584|NP_000537|NP_001263627|NP_001263626|NP_001263625|NP_001119588|NP_001263628|NP_001119587|NP_001263624|NP_001119585|NP_001263690|NP_001119589|NP_00126368920700.3%
 
Other values (17525)66582795.4%
 
2020-11-18T23:51:06.416525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2574 ?
Unique (%)0.4%
2020-11-18T23:51:06.576936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1748
Median length22
Mean length36.37391409
Min length1

SWISS-PROT Accessions Interactor B
Categorical

HIGH CARDINALITY

Distinct21620
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
-
 
7364
P05067
 
2098
P04637
 
1866
P0CG48
 
1391
Q00987
 
982
Other values (21615)
684101 
ValueCountFrequency (%) 
-73641.1%
 
P0506720980.3%
 
P0463718660.3%
 
P0CG4813910.2%
 
Q009879820.1%
 
Q135479460.1%
 
P079008850.1%
 
P111428700.1%
 
P628058300.1%
 
Q094727560.1%
 
Other values (21610)67981497.4%
 
2020-11-18T23:51:06.904982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4853 ?
Unique (%)0.7%
2020-11-18T23:51:07.054900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length34
Median length6
Mean length6.000561764
Min length1

TREMBL Accessions Interactor B
Categorical

HIGH CARDINALITY

Distinct8680
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
-
362429 
B4DGD0|E9PG40|B4DJT9
 
2098
H2EHT1|A0A087WT22|A0A087WXZ1|Q53GA5|A0A087X1Q1|K7PPA8
 
1866
A7UKX8|Q96DS0|A7UKX9|A7UKX7|G3XA89
 
982
Q6IT96
 
946
Other values (8675)
329481 
ValueCountFrequency (%) 
-36242951.9%
 
B4DGD0|E9PG40|B4DJT920980.3%
 
H2EHT1|A0A087WT22|A0A087WXZ1|Q53GA5|A0A087X1Q1|K7PPA818660.3%
 
A7UKX8|Q96DS0|A7UKX9|A7UKX7|G3XA899820.1%
 
Q6IT969460.1%
 
Q86SX1|K9JA468850.1%
 
V9HW22|Q53HF28700.1%
 
B2R4R08300.1%
 
Q7Z6C17560.1%
 
Q96IF9|V9HW807250.1%
 
Other values (8670)32541546.6%
 
2020-11-18T23:51:07.214414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1599 ?
Unique (%)0.2%
2020-11-18T23:51:07.380108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length200
Median length1
Mean length6.177970255
Min length1

REFSEQ Accessions Interactor B
Categorical

HIGH CARDINALITY

Distinct22371
Distinct (%)3.2%
Missing598
Missing (%)0.1%
Memory size5.3 MiB
-
 
4033
NP_958817|NP_958816|NP_001191230|NP_001191231|NP_001191232|NP_000475|NP_001372182|NP_001129488|NP_001129603|NP_001129601|NP_001129602
 
2098
NP_001119586|NP_001119590|NP_001119584|NP_000537|NP_001263627|NP_001263626|NP_001263625|NP_001119588|NP_001263628|NP_001119587|NP_001263624|NP_001119585|NP_001263690|NP_001119589|NP_001263689
 
1866
NP_066289
 
1391
NP_001354919|NP_001265391|NP_002383|NP_001138811|NP_001138812|NP_001138809
 
982
Other values (22366)
686834 
ValueCountFrequency (%) 
-40330.6%
 
NP_958817|NP_958816|NP_001191230|NP_001191231|NP_001191232|NP_000475|NP_001372182|NP_001129488|NP_001129603|NP_001129601|NP_00112960220980.3%
 
NP_001119586|NP_001119590|NP_001119584|NP_000537|NP_001263627|NP_001263626|NP_001263625|NP_001119588|NP_001263628|NP_001119587|NP_001263624|NP_001119585|NP_001263690|NP_001119589|NP_00126368918660.3%
 
NP_06628913910.2%
 
NP_001354919|NP_001265391|NP_002383|NP_001138811|NP_001138812|NP_0011388099820.1%
 
NP_0049559460.1%
 
NP_005339|NP_0010179638850.1%
 
NP_694881|NP_0065888700.1%
 
NP_001349772|NP_0014207560.1%
 
NP_009057|NP_001341857|NP_0013418567250.1%
 
Other values (22361)68265297.8%
 
2020-11-18T23:51:07.594563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5303 ?
Unique (%)0.8%
2020-11-18T23:51:07.763653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1748
Median length22
Mean length36.95694481
Min length1

Ontology Term IDs
Categorical

HIGH CARDINALITY

Distinct480
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
-
656309 
BTO:0000007
 
10580
BTO:0000567
 
9542
BTO:0002181
 
5495
HP:0001507
 
3306
Other values (475)
 
12570
ValueCountFrequency (%) 
-65630994.1%
 
BTO:0000007105801.5%
 
BTO:000056795421.4%
 
BTO:000218154950.8%
 
HP:000150733060.5%
 
BTO:000066417460.3%
 
PATO:0000169|HP:000150713830.2%
 
HP:0001507|PATO:000016913480.2%
 
HP:0001507|BTO:000066411360.2%
 
BTO:0000664|HP:000150710840.2%
 
Other values (470)58730.8%
 
2020-11-18T23:51:07.931350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique218 ?
Unique (%)< 0.1%
2020-11-18T23:51:08.081527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length66
Median length1
Mean length1.722308047
Min length1

Ontology Term Names
Categorical

HIGH CARDINALITY

Distinct474
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
-
656309 
HEK-293 cell
 
10580
HeLa cell
 
9542
HEK-293T cell
 
5495
Growth abnormality
 
3306
Other values (469)
 
12570
ValueCountFrequency (%) 
-65630994.1%
 
HEK-293 cell105801.5%
 
HeLa cell95421.4%
 
HEK-293T cell54950.8%
 
Growth abnormality33060.5%
 
K-562 cell17460.3%
 
viability|Growth abnormality13830.2%
 
Growth abnormality|viability13480.2%
 
Growth abnormality|K-562 cell11360.2%
 
K-562 cell|Growth abnormality10840.2%
 
Other values (464)58730.8%
 
2020-11-18T23:51:08.234216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique213 ?
Unique (%)< 0.1%
2020-11-18T23:51:08.390982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length132
Median length1
Mean length1.861092402
Min length1

Ontology Term Categories
Categorical

HIGH CARDINALITY

Distinct104
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
-
656309 
cell line
 
28527
phenotype
 
4447
phenotype|phenotype
 
2822
phenotype|cell type
 
1427
Other values (99)
 
4270
ValueCountFrequency (%) 
-65630994.1%
 
cell line285274.1%
 
phenotype44470.6%
 
phenotype|phenotype28220.4%
 
phenotype|cell type14270.2%
 
cell type|phenotype14200.2%
 
cell type7060.1%
 
disease|cell line4920.1%
 
cell line|disease4840.1%
 
tissue303< 0.1%
 
Other values (94)8650.1%
 
2020-11-18T23:51:08.550404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique48 ?
Unique (%)< 0.1%
2020-11-18T23:51:08.697442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length84
Median length1
Mean length1.588145348
Min length1

Ontology Term Qualifier IDs
Categorical

HIGH CARDINALITY

Distinct119
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
-
674907 
DOID:3702
 
9214
-|-
 
6731
DOID:8552
 
1737
BTO:0000414
 
1588
Other values (114)
 
3625
ValueCountFrequency (%) 
-67490796.7%
 
DOID:370292141.3%
 
-|-67311.0%
 
DOID:855217370.2%
 
BTO:000041415880.2%
 
-|-|-5120.1%
 
CL:00000665100.1%
 
DOID:5603338< 0.1%
 
BTO:0001109309< 0.1%
 
DOID:3717291< 0.1%
 
Other values (109)16650.2%
 
2020-11-18T23:51:08.847507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique38 ?
Unique (%)< 0.1%
2020-11-18T23:51:09.142401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length48
Median length1
Mean length1.211706186
Min length1

Ontology Term Qualifier Names
Categorical

HIGH CARDINALITY

Distinct118
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
-
674907 
cervical adenocarcinoma
 
9214
-|-
 
6731
epithelial cell
 
2098
chronic myeloid leukemia
 
1737
Other values (113)
 
3115
ValueCountFrequency (%) 
-67490796.7%
 
cervical adenocarcinoma92141.3%
 
-|-67311.0%
 
epithelial cell20980.3%
 
chronic myeloid leukemia17370.2%
 
-|-|-5120.1%
 
acute T cell leukemia338< 0.1%
 
HCT-116 cell309< 0.1%
 
gastric adenocarcinoma291< 0.1%
 
breast adenocarcinoma256< 0.1%
 
Other values (108)14090.2%
 
2020-11-18T23:51:09.293053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique38 ?
Unique (%)< 0.1%
2020-11-18T23:51:09.452365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length93
Median length1
Mean length1.479127317
Min length1
Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
-
690218 
-|-
 
6779
-|-|-
 
522
undetermined
 
61
wild type
 
53
Other values (11)
 
169
ValueCountFrequency (%) 
-69021898.9%
 
-|-67791.0%
 
-|-|-5220.1%
 
undetermined61< 0.1%
 
wild type53< 0.1%
 
-|-|-|-52< 0.1%
 
partial rescue45< 0.1%
 
-|-|-|-|-42< 0.1%
 
wild type|-7< 0.1%
 
-|wild type6< 0.1%
 
Other values (6)17< 0.1%
 
2020-11-18T23:51:09.594501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)< 0.1%
2020-11-18T23:51:09.721151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length44
Median length1
Mean length1.02652185
Min length1

Organism Name Interactor A
Categorical

HIGH CARDINALITY

Distinct55
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
Homo sapiens
649680 
Severe acute respiratory syndrome coronavirus 2
 
19336
Mus musculus
 
18982
Saccharomyces cerevisiae (S288c)
 
2378
Human Immunodeficiency Virus 1
 
1670
Other values (50)
 
5756
ValueCountFrequency (%) 
Homo sapiens64968093.1%
 
Severe acute respiratory syndrome coronavirus 2193362.8%
 
Mus musculus189822.7%
 
Saccharomyces cerevisiae (S288c)23780.3%
 
Human Immunodeficiency Virus 116700.2%
 
Severe acute respiratory syndrome-related coronavirus12470.2%
 
Rattus norvegicus8760.1%
 
Human Herpesvirus 87840.1%
 
Hepatitus C Virus5170.1%
 
Human Herpesvirus 43600.1%
 
Other values (45)19720.3%
 
2020-11-18T23:51:09.860250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique6 ?
Unique (%)< 0.1%
2020-11-18T23:51:10.001398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length53
Median length12
Mean length13.2172536
Min length10

Organism Name Interactor B
Categorical

HIGH CARDINALITY

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
Homo sapiens
689010 
Mus musculus
 
4321
Saccharomyces cerevisiae (S288c)
 
1115
Rattus norvegicus
 
991
Schizosaccharomyces pombe (972h)
 
586
Other values (47)
 
1779
ValueCountFrequency (%) 
Homo sapiens68901098.7%
 
Mus musculus43210.6%
 
Saccharomyces cerevisiae (S288c)11150.2%
 
Rattus norvegicus9910.1%
 
Schizosaccharomyces pombe (972h)5860.1%
 
Human Immunodeficiency Virus 13630.1%
 
Bos taurus147< 0.1%
 
Arabidopsis thaliana (Columbia)129< 0.1%
 
Human Herpesvirus 1110< 0.1%
 
Drosophila melanogaster86< 0.1%
 
Other values (42)9440.1%
 
2020-11-18T23:51:10.153283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique8 ?
Unique (%)< 0.1%
2020-11-18T23:51:10.299968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length53
Median length12
Mean length12.08652311
Min length8

Interactions

2020-11-18T23:49:16.560682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:17.003941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:17.374583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:17.727845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:18.102913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:18.490246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:18.864112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:19.211937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:19.580257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:19.933151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:20.260035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:20.594228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:20.946357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:21.283369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:21.645601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:21.988972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:22.338698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:22.931558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:23.321280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:23.668565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:24.009411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:24.349071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:24.677842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:25.007890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:25.470916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:25.810165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:26.147773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:26.481554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:26.812896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:27.175198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:27.513581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:27.850451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:28.180610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:28.504834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:28.827969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:29.159190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-18T23:51:10.402389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-18T23:51:10.583905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-18T23:51:10.763972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-18T23:51:10.956682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-11-18T23:51:11.197334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-11-18T23:49:32.207037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:36.279132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:40.744361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-18T23:49:42.259371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

#BioGRID Interaction IDEntrez Gene Interactor AEntrez Gene Interactor BBioGRID ID Interactor ABioGRID ID Interactor BSystematic Name Interactor ASystematic Name Interactor BOfficial Symbol Interactor AOfficial Symbol Interactor BSynonyms Interactor ASynonyms Interactor BExperimental SystemExperimental System TypeAuthorPublication SourceOrganism ID Interactor AOrganism ID Interactor BThroughputScoreModificationQualificationsTagsSource DatabaseSWISS-PROT Accessions Interactor ATREMBL Accessions Interactor AREFSEQ Accessions Interactor ASWISS-PROT Accessions Interactor BTREMBL Accessions Interactor BREFSEQ Accessions Interactor BOntology Term IDsOntology Term NamesOntology Term CategoriesOntology Term Qualifier IDsOntology Term Qualifier NamesOntology Term TypesOrganism Name Interactor AOrganism Name Interactor B
010364162318112315108607--MAP2K4FLNCJNKK|JNKK1|MAPKK4|MEK4|MKK4|PRKMK4|SAPKK-1|SAPKK1|SEK1|SERK1|SKK1ABP-280|ABP280A|ABPA|ABPL|FLN2|MFM5|MPD4Two-hybridphysicalMarti A (1997)PUBMED:900689596069606Low Throughput----BIOGRIDP45985-NP_003001|NP_001268364Q14315Q59H94NP_001120959|NP_001449------Homo sapiensHomo sapiens
11178466588124185106603--MYPNACTN2CMD1DD|CMH22|MYOP|RCM4CMD1AATwo-hybridphysicalBang ML (2001)PUBMED:1130942096069606Low Throughput----BIOGRIDQ86TC9A0A087WX60NP_001243197|NP_001243196|NP_115967P35609Q59FD9|F6THM6NP_001094|NP_001265272|NP_001265273------Homo sapiensHomo sapiens
2183902339106605108625--ACVR1FNTAACTRI|ACVR1A|ACVRLK2|ALK2|FOP|SKR1|TSRIFPTA|PGGT1A|PTAR2Two-hybridphysicalWang T (1996)PUBMED:859908996069606Low Throughput----BIOGRIDQ04771D3DPA4NP_001104537|NP_001096P49354-NP_002018------Homo sapiensHomo sapiens
327826245371108894111384--GATA2PMLDCML|IMD21|MONOMAC|NFE1BMYL|PP8675|RNF71|TRIM19Two-hybridphysicalTsuzuki S (2000)PUBMED:1093810496069606Low Throughput----BIOGRIDP23769-NP_001139134|NP_116027|NP_001139133P29590-NP_150250|NP_150253|NP_150252|NP_150247|NP_150241|NP_150242|NP_150243|NP_002666|NP_150249------Homo sapiensHomo sapiens
441861186774112038112651RP4-547C9.3-RPA2STAT3REPA2|RP-A p32|RP-A p34|RPA32ADMIO|APRF|HIESTwo-hybridphysicalKim J (2000)PUBMED:1087589496069606Low Throughput----BIOGRIDP15927B4DUL2NP_001342057|NP_002937|NP_001284487|NP_001342058|NP_001273005P40763-NP_644805|NP_003141|NP_001356447|NP_001356443|NP_001371920|NP_001371913|NP_001371917|NP_001356445|NP_001356446|NP_998827|NP_001371915|NP_001371914|NP_001356442|NP_001371918|NP_001371919|NP_001371921|NP_001371922|NP_001371916|NP_001356448|NP_001356449|NP_001356441------Homo sapiensHomo sapiens
558637523163106870116775--ARF1GGA3--Two-hybridphysicalDell'Angelica EC (2000)PUBMED:1074708996069606Low Throughput----BIOGRIDP84077A0A024R3Q3NP_001019398|NP_001019397|NP_001649|NP_001019399Q9NZ52B7Z456|A8K6M0NP_619525|NP_001278571|NP_001278570|NP_001166175|NP_001166174|NP_054720------Homo sapiensHomo sapiens
661237723647106872117174--ARF3ARFIP2-POR1Two-hybridphysicalKanoh H (1997)PUBMED:903814296069606Low Throughput----BIOGRIDP61204A0A024R0Y6NP_001650P53365B4DUZ3|B4DXH2|A0A087X1E4NP_036534|NP_001229785|NP_001229784|NP_001229783------Homo sapiensHomo sapiens
761737727236106872118084--ARF3ARFIP1-HSU52521Two-hybridphysicalKanoh H (1997)PUBMED:903814296069606Low Throughput----BIOGRIDP61204A0A024R0Y6NP_001650P53367B4E273|B4DS69|Q8N8M9NP_001020764|NP_001020766|NP_001274362|NP_001274360|NP_001274361|NP_055262------Homo sapiensHomo sapiens
866354464226119970106728--XRN1ALDOASEP1ALDA|GSD12|HEL-S-87pTwo-hybridphysicalLehner B (2004)PUBMED:1523174796069606High Throughput----BIOGRIDQ8IZH2-NP_001269786|NP_061874|NP_001269788P04075V9HWN7NP_001121089|NP_908932|NP_908930|NP_001230106|NP_000025------Homo sapiensHomo sapiens
986635110513106848115769--APPAPPBP2AAA|ABETA|ABPP|AD1|APPI|CTFgamma|CVAP|PN-II|PN2APP-BP2|HS.84084|PAT1Two-hybridphysicalZheng P (1998)PUBMED:984396096069606Low Throughput----BIOGRIDP05067B4DGD0|E9PG40|B4DJT9NP_958817|NP_958816|NP_001191230|NP_001191231|NP_001191232|NP_000475|NP_001372182|NP_001129488|NP_001129603|NP_001129601|NP_001129602Q92624A0A024QZ47NP_001269405|NP_006371------Homo sapiensHomo sapiens

Last rows

#BioGRID Interaction IDEntrez Gene Interactor AEntrez Gene Interactor BBioGRID ID Interactor ABioGRID ID Interactor BSystematic Name Interactor ASystematic Name Interactor BOfficial Symbol Interactor AOfficial Symbol Interactor BSynonyms Interactor ASynonyms Interactor BExperimental SystemExperimental System TypeAuthorPublication SourceOrganism ID Interactor AOrganism ID Interactor BThroughputScoreModificationQualificationsTagsSource DatabaseSWISS-PROT Accessions Interactor ATREMBL Accessions Interactor AREFSEQ Accessions Interactor ASWISS-PROT Accessions Interactor BTREMBL Accessions Interactor BREFSEQ Accessions Interactor BOntology Term IDsOntology Term NamesOntology Term CategoriesOntology Term Qualifier IDsOntology Term Qualifier NamesOntology Term TypesOrganism Name Interactor AOrganism Name Interactor B
69779228763201425459870374383881112895G128_gp06-ORF5TFRCMERS-CoV ORF5|ORF5_CVEMC|PRO_0000422438CD71|T9|TFR|TFR1|TR|TRFR|p90Affinity Capture-MSphysicalGordon DE (2020)PUBMED:3306019713356269606High Throughput0.73055-High confidence interactions were identified using a two step filtering process with the final criteria including a MiST score >= 0.6, SAINTexpress BFDR =< 0.05 and average spectral counts >= 2. The MiST score is provided in the score column.-BIOGRIDK9N7D2-YP_009047208P02786A8K6Q8NP_001121620|NP_003225|NP_001300894|NP_001300895------Middle-East Respiratory Syndrome-related CoronavirusHomo sapiens
69779328763211425459882664383881113885G128_gp06XX-FW89031B12.1ORF5UBL4AMERS-CoV ORF5|ORF5_CVEMC|PRO_0000422438DX254E|DXS254E|G6PD|GDX|GET5|MDY2|TMA24|UBL4Affinity Capture-MSphysicalGordon DE (2020)PUBMED:3306019713356269606High Throughput0.76045-High confidence interactions were identified using a two step filtering process with the final criteria including a MiST score >= 0.6, SAINTexpress BFDR =< 0.05 and average spectral counts >= 2. The MiST score is provided in the score column.-BIOGRIDK9N7D2-YP_009047208P11441-NP_055050------Middle-East Respiratory Syndrome-related CoronavirusHomo sapiens
69779428763221425459879174383881113647G128_gp06DADB-70P7.10-021ORF5BAG6MERS-CoV ORF5|ORF5_CVEMC|PRO_0000422438BAG-6|BAT3|D6S52E|G3Affinity Capture-MSphysicalGordon DE (2020)PUBMED:3306019713356269606High Throughput0.8006-High confidence interactions were identified using a two step filtering process with the final criteria including a MiST score >= 0.6, SAINTexpress BFDR =< 0.05 and average spectral counts >= 2. The MiST score is provided in the score column.-BIOGRIDK9N7D2-YP_009047208P46379-NP_001092004|NP_004630|NP_542434|NP_001186627|NP_001186626|NP_542433------Middle-East Respiratory Syndrome-related CoronavirusHomo sapiens
697795287632314254598107124383881115938G128_gp06-ORF5FAM189BMERS-CoV ORF5|ORF5_CVEMC|PRO_0000422438C1orf2|COTE1Affinity Capture-MSphysicalGordon DE (2020)PUBMED:3306019713356269606High Throughput0.98371-High confidence interactions were identified using a two step filtering process with the final criteria including a MiST score >= 0.6, SAINTexpress BFDR =< 0.05 and average spectral counts >= 2. The MiST score is provided in the score column.-BIOGRIDK9N7D2-YP_009047208P81408Q9Y6J7NP_001254537|NP_006580|NP_937995------Middle-East Respiratory Syndrome-related CoronavirusHomo sapiens
697796287632414254598231554383881116769G128_gp06RP11-475E11.6ORF5CLCC1MERS-CoV ORF5|ORF5_CVEMC|PRO_0000422438MCLCAffinity Capture-MSphysicalGordon DE (2020)PUBMED:3306019713356269606High Throughput0.8311-High confidence interactions were identified using a two step filtering process with the final criteria including a MiST score >= 0.6, SAINTexpress BFDR =< 0.05 and average spectral counts >= 2. The MiST score is provided in the score column.-BIOGRIDK9N7D2-YP_009047208Q96S66A0A024R0G0|A0A024R095NP_001364389|NP_001364399|NP_001364398|NP_001364395|NP_001364394|NP_001364397|NP_001364396|NP_001364391|NP_001364390|NP_001364393|NP_001364392|NP_055942|NP_001364387|NP_001364388|NP_001265132|NP_001265131|NP_001041675------Middle-East Respiratory Syndrome-related CoronavirusHomo sapiens
69779728763251425459439564383879110147G128_gp02-SLGALS1spike|MERS-CoV S|MERS-CoV spike|S protein|surface|SPIKE_CVEMC|PRO_0000422465GAL1|GBPAffinity Capture-MSphysicalGordon DE (2020)PUBMED:3306019713356269606High Throughput0.81988-High confidence interactions were identified using a two step filtering process with the final criteria including a MiST score >= 0.6, SAINTexpress BFDR =< 0.05 and average spectral counts >= 2. The MiST score is provided in the score column.-BIOGRIDK9N5Q8-YP_009047204P09382-NP_002296------Middle-East Respiratory Syndrome-related CoronavirusHomo sapiens
6977982876326-98684383874115201--ORF9bTOMM70ASARS-CoV2 ORF9b|SARS-CoV-2 ORF9b|9b|ORF9B_SARS2|PRO_0000449657-Co-crystal StructurephysicalGordon DE (2020)PUBMED:3306019726970499606Low Throughput--Electron microscopy (EM) structure-BIOGRIDP0DTD2--O94826-NP_055635------Severe acute respiratory syndrome coronavirus 2Homo sapiens
697799287632743740577237654383873117265GU280_gp09-ORF8IL17RASARS-CoV2 ORF8|SARS-CoV-2 ORF8|8|NS7B_SARS2|PRO_0000449655CANDF5|CD217|CDw217|IL-17RA|IL17R|hIL-17RAffinity Capture-WesternphysicalGordon DE (2020)PUBMED:3306019726970499606Low Throughput----BIOGRIDP0DTC8-YP_009724396Q96F46-NP_001276834|NP_055154------Severe acute respiratory syndrome coronavirus 2Homo sapiens
6978002876328-98684383874115201--ORF9bTOMM70ASARS-CoV2 ORF9b|SARS-CoV-2 ORF9b|9b|ORF9B_SARS2|PRO_0000449657-Affinity Capture-WesternphysicalGordon DE (2020)PUBMED:3306019726970499606Low Throughput----BIOGRIDP0DTD2--O94826-NP_055635------Severe acute respiratory syndrome coronavirus 2Homo sapiens
6978012876329-98684383874115201--ORF9bTOMM70ASARS-CoV2 ORF9b|SARS-CoV-2 ORF9b|9b|ORF9B_SARS2|PRO_0000449657-Co-purificationphysicalGordon DE (2020)PUBMED:3306019726970499606Low Throughput----BIOGRIDP0DTD2--O94826-NP_055635------Severe acute respiratory syndrome coronavirus 2Homo sapiens